Mosaic AI is a suite of tools that allows Databricks users to build, manage, and deploy software solutions that incorporate AI, ML, and large language model (LLM) technologies. Mosaic AI is fully integrated within the Databricks Data Intelligence Platform, which provides a single solution for storing data in a unified data lakehouse, training AI and machine learning models, and deploying those AI/ML solutions in production. Databricks Mosaic AI encompasses the following products: 💠 Mosaic AI Vector Search - A queryable vector database integrated with the Databricks Platform, Mosaic AI Vector Search is used in LLM solutions to store and retrieve mathematical representations of the semantic contents of text or image data. 💠 Mosaic AI Agent Framework - A set of Databricks tools that allow developers to build, deploy, and evaluate AI agents using Retrieval Augmented Generation (RAG), an AI design technique that augments an existing LLM with an external knowledge base. 💠Mosaic AI Model Serving - A solution for deploying LLMs and accessing Gen-AI models, including open LLMs (via Foundation Model APIs) and external LLMs hosted outside Databricks. 💠Mosaic AI Gateway - A tool for managing the usage of Gen-AI models, Mosaic AI Gateway delivers monitoring, governance, and production readiness features like usage tracking, access permissions, and traffic routing. 💠Mosaic AI Model Training - An AI model training solution that allows users to customize open-source LLMs or cost-effectively train new ones using enterprise data. 💠 Feature Store - A solution for creating, publishing, and re-using features used to train ML models or feed batch inference pipelines. 💠 Databricks AutoML - Databricks AutoML is a solution that provides a low-code approach to building, training, and deploying ML models. 💠 MLflow - MLflow is an open-source platform used to manage artifacts and workflows throughout the MLOps pipeline - from initial model development and training, through to deployment and operation. 💠Lakehouse Monitoring - A tool for monitoring data quality in the data lakehouse, Lakehouse Monitoring can also be used to track the performance of ML models and model-serving endpoints. Though not technically a Mosaic AI product, Databricks Unity Catalog is another important service that provides centralized discovery, management, and governance of models and data stored in the Databricks lakehouse. Learn more about Databricks Mosaic AI use cases: https://lnkd.in/dy4-aXCV
